جست‌وجوی استاد و پژوهشگر

• Stochastic Programming
• Mathematical Modeling
• Financial Mathematics
• +۷ مورد دیگر

Prof. habil. dr. Leonidas Sakalauskas is an Affiliated Scientist at the Smart Technologies Research Group within the Institute of Data Science and Digital Technologies at Vilnius University. His extensive research career spans mathematical modeling, stochastic programming, and statistical analysis with significant contributions across multiple disciplines. He maintains an active research profile with recent publications extending into 2025. Professor Sakalauskas' research focuses on stochastic programming, mathematical modeling, financial mathematics, statistical analysis, optimization, and queueing theory. His work demonstrates a consistent pattern of interdisciplinary research, bridging theoretical mathematics with practical applications in finance, social sciences, and computer science. He has developed innovative approaches to agent-based modeling, fractal analysis, and Monte Carlo methods, contributing significantly to both theoretical frameworks and practical implementations. His recent publications reveal a continuing evolution of research interests, with increasing emphasis on data science applications, computational social science, and advanced statistical modeling techniques. The articles demonstrate sophisticated methodological approaches to complex problems across diverse domains including financial modeling, social cohesion analysis, and network performance optimization. Throughout his career, Professor Sakalauskas has maintained a strong publication record in high-impact journals and conference proceedings, reflecting his ongoing engagement with the international research community. His work shows consistent methodological rigor combined with practical relevance across multiple application domains. Professor Sakalauskas has collaborated extensively with researchers across Lithuania and internationally, demonstrating strong teamwork and interdisciplinary engagement. His research has practical applications in financial modeling, social science analysis, and computational systems optimization, contributing to both theoretical advances and real-world implementations.

Michael Whittمشاهده پروفایل

دانشیار

• Hemodynamics
• Product Development
• Vascular Mechanics
• +۳ مورد دیگر

Michael Whitt serves as Associate Professor in the Biomedical Engineering Department at California Polytechnic State University, specializing in hemodynamics, vascular mechanics, and medical device product development. He holds five medical device patents and co-founded Cordex Systems Inc., which developed a noninvasive cardiovascular disease detection method. His expertise spans pharmaceutical process development, medical device startups, and entrepreneurship education. His academic credentials include: Ph.D. in Biomedical Engineering from Rutgers University/University of Medicine and Dentistry of New Jersey M.S. in Biomedical Engineering from Rutgers University/University of Medicine and Dentistry of New Jersey MBA from UCLA Anderson School of Management B.S. in Chemical Engineering from Purdue University Dr. Whitt's research integrates biomechanics with clinical innovation , focusing on noninvasive cardiovascular diagnostics and vascular assessment technologies. His work in medical device development bridges engineering principles with real-world healthcare applications, particularly through patent-protected technologies for endothelial function measurement. He actively promotes entrepreneurship through curriculum design, including Purdue University's product development course and Notre Dame's ESTEEM program, emphasizing 'learn-by-doing' pathways for underrepresented students. Analysis of his 2017-2025 publications reveals three dominant research trajectories: (1) cardiovascular diagnostics (70% of output) featuring novel devices for arterial reactivity assessment; (2) sustainable medical device manufacturing (20%) exploring nanostructured coatings for dry machining; and (3) engineering education (10%) with recent clinical immersion frameworks. His diagnostic work consistently targets operator-independent, noninvasive methodologies validated against obesity/fitness metrics. Recognition includes: 2008 Center for Leadership Development Achievement in Science and Technical Disciplines Award for advancing minority youth in STEM He has developed educational initiatives at Miami Dade College, the Center for Leadership Development (Indianapolis), and the Academy of Business Leadership (Rosemead), focusing on product development curricula and pathways to financial self-sufficiency for high-potential, low-opportunity students. His industry collaborations span medical device manufacturing process optimization and community-based health technology education programs.

• Machine Learning
• Deep Learning
• Recommender Systems
• +۷ مورد دیگر

Dr. Ruihai Dong is an Assistant Professor at the School of Computer Science, University College Dublin, affiliated with the Insight Centre for Data Analytics. His research bridges Machine Learning, Deep Learning, and Recommender Systems with significant applications in finance and medical imaging. Develops AI-driven solutions for financial markets and healthcare Collaborates with industry partners like Eagle Alpha and Samsung Funded by Enterprise Ireland for commercialization studies Research focuses on: Recommender systems (news, finance, conversational AI) Graph neural networks for financial risk modeling Medical imaging under limited labeling constraints Algorithmic transparency and ethical AI applications 3D CNNs & transformers for seismic analysis Large language models for multilingual evaluation Recent publications emphasize: Hybrid architectures for multimodal data Explainable AI techniques in financial and news domains Geometric deep learning for microstructure and seismic data Curriculum strategies in active learning Media bias mitigation frameworks Latent space modeling for asset embeddings Scientific contributions recognized through: Outstanding Research Award 2018 by UCD School of Computer Science Industry collaborations include work with: Eagle Alpha (financial analytics) SkillPages (data-driven platforms) Samsung (technology innovation)

Patrick Rebeschiniمشاهده پروفایل

مدرس

• High-Dimensional Probability
• Statistics
• Optimization
• +۴ مورد دیگر

Patrick Rebeschini is a Lecturer in the Department of Statistics at the University of Oxford. Before joining Oxford, he held positions at Yale University, including Lecturer in Computer Science and Postdoctoral Associate at the Yale Institute for Network Science. He earned his Ph.D. in Operations Research and Financial Engineering from Princeton University, supervised by Ramon van Handel. His research spans high-dimensional probability, statistics, and optimization, focusing on designing computationally efficient and statistically optimal algorithms for machine learning and artificial intelligence. His recent activities include organizing workshops on Online Learning and Game-Theoretic Statistics (2025), serving as Senior Area Chair for ICML 2025, and chairing sessions at the IMS Annual Meeting (2022). He co-organizes the joint Maths-Stats colloquium series at Oxford and contributes to doctoral training as a Co-Investigator for the Imperial-Oxford StatML Centre for Doctoral Training (CDT) and the Fundamentals of AI Erlangen Hub. Scientific Awards: 2019 Oxford MPLS Teaching Award Excellence in Teaching Award from Princeton Engineering Council (2013) Grants & Collaborations: Co-Investigator for StatML CDT and Fundamentals of AI Erlangen Hub Member of Bernoulli Society, Institute of Mathematical Statistics, and ELLIS Teaching: Organized reading groups on learning theory (2017–2025) Module Leader for doctoral courses on Online Learning and Reinforcement Learning (2023) Supervisor for UNIQ+ DeepMind summer interns (2022–present)

Bharatendra Raiمشاهده پروفایل

استاد

• Business Analytics
• Data Mining
• Deep Learning
• +۶ مورد دیگر

Dr. Bharatendra Rai is a Professor and Chairperson of the Decision & Information Sciences department at the University of Massachusetts Dartmouth's Charlton College of Business. His academic and research profile spans over two decades with contributions to business analytics, data mining, and reliability engineering. He teaches core business courses like MIS 101: The Business Organization and POM 212: Business Statistics , focusing on operations management and data-driven decision-making. PhD in Industrial Engineering (Wayne State University, 2004) MTech in Quality, Reliability & OR (Indian Statistical Institute, 1993) MSc in Statistics (Meerut University, 1991) His research interests include business analytics , deep learning , big data research , and reliability prediction , with applications in healthcare, manufacturing, and financial services. Recent publications cover topics from LSTM neural networks for sentiment classification to quantum computing in healthcare and energy efficiency optimization in renewable projects. Advising and grants: No formal advisees listed, but he has mentored students through University of Massachusetts Dartmouth's Big Data Club (winner of DataFest 2023's Best Data Visualization) and Data Challenge Kaggle initiatives. Collaborative projects include partnerships with NVIDIA and Dell on AI research.

Jens Josephsonمشاهده پروفایل

استاد

• Corporate Finance
• Insolvency Resolution
• Credit Rating Systems
• +۳ مورد دیگر

Jens Josephson is a Professor at Stockholm Business School, Stockholm University, with research spanning corporate finance, game theory, and labor economics. His work addresses critical areas such as insolvency resolution, credit rating systems, and stochastic dynamics in finite games. Key Research Areas: Corporate restructuring, bond market development, and evolutionary game theory applications Recent Publications: Focus on structured finance mechanisms and stochastic adaptation models (2017-2023) His analytical approach combines economic theory with empirical studies, particularly in labor market dynamics and financial system stability. Current research includes better-reply game dynamics and work ethic evolution over time. Contact: jens.josephson@sbs.su.se

Katarina Boyeمشاهده پروفایل

مدرس ارشد

• Sociology
• Gender Studies
• Social Stratification
• +۵ مورد دیگر

Katarina Boye is a Senior Lecturer at Stockholm University's Swedish Institute for Social Research, where she leads the Level of Living Survey 2020 (LNU2020). Her research focuses on family structures, gendered labor division, and work-care balance in both different-sex and same-sex couples. Academic affiliation: Swedish Institute for Social Research (Stockholm University) Leadership role: Director of nationally representative Level of Living Survey Key research areas include: Parental leave distribution and labor market consequences Childcare leave wage penalties Gender dynamics in household work Comparative analysis of same-sex vs different-sex couples Long-term effects of early parenthood on careers Her recent publications examine pandemic-era work-care renegotiation, longitudinal outcomes of parental leave patterns, and socioeconomic factors in same-sex couple childbearing decisions. She utilizes Swedish population register data and mixed-method approaches. Selected methodological approaches: Discrete-time event history analysis Linear probability modeling Qualitative in-depth interviews Quantitative regression models

Yoram Margaliothمشاهده پروفایل

استاد

• Tax Law
• Public Finance
• International Taxation
• +۴ مورد دیگر

Yoram Margalioth is a Full Professor at Tel Aviv University Faculty of Law, where he teaches Taxation, Tax Policy (Public Finance), International Taxation, and Economic Growth and Distributive Justice. He pioneered Israel's first law MOOC on Coursera and has held visiting positions at NYU, Harvard, Toronto, and NUS. His academic leadership includes co-founding the Law and Philanthropy Institute (2014-2019) and the Microbusiness and Economic Justice Clinical Program. His educational background features a J.S.D. (1997) and LL.M. in Taxation (1995) from NYU School of Law, and an LL.B. (1991) and LL.M. Magna Cum Laude (1994) from Hebrew University School of Law. His doctoral thesis on U.S. tax code inflation adjustment was supervised by Daniel Shaviro. Margalioth's research integrates tax law with socioeconomic justice, focusing on Ultra-Orthodox communities, climate policy, and security studies. His work examines tax asymmetry in financial markets, welfare stigma, and charitable incentives, often bridging legal theory with empirical economics. He critically analyzes policy instruments like carbon taxes and per-capita taxation models while addressing cultural dimensions of economic behavior. His publication trajectory reveals deepening interdisciplinary engagement, shifting from pure tax technicalities toward macroeconomic justice and security policy. Recent works increasingly incorporate behavioral economics and cross-cultural analysis, particularly regarding Haredi society, while maintaining rigorous tax policy foundations. Climate change taxation and airport security profiling now feature prominently alongside traditional tax scholarship. American Law and Economics Association Prize for Best paper (for 'A Case for Pay Secrecy') As Academic Supervisor of the Microbusiness Clinical Program, Margalioth mentored students working with Ultra-Orthodox women in Bnei Brak. His World Bank consultancy (2003) and Harvard International Tax Program deputy directorship (2002-2003) demonstrate policy impact. Current research receives institutional support through TAU's Law and Philanthropy Institute framework. He co-founded and directed the Law and Philanthropy Institute (2014-2019), established the Microbusiness and Economic Justice Clinical Program (2004-2008), and served as Senior Fellow at The Haredi Institute for Public Affairs until 2021. These initiatives operationalize his research on economic inclusion, particularly for marginalized religious communities.

• ESG Investing
• Sustainable Finance
• Corporate Finance
• +۴ مورد دیگر

Anna Grygiel-Tomaszewska serves as Assistant Professor and Head of the Department of Corporate Financial Management within the College of Management and Finance at Warsaw School of Economics. Her university service includes: Member, University Recruitment Committee Member, Committee for Sustainable Development of the Warsaw School of Economics Her research centers on sustainable finance with emphasis on ESG factors, corporate finance, and financial market dynamics. Key focus areas include ESG integration in risk management, sustainable transformation challenges, financialization processes, and high-frequency trading mechanisms. She investigates these topics within European and Polish financial contexts, particularly examining real estate funds, climate risk implications, and AI's impact on market efficiency. Analysis of her 15 most recent publications (2016-2024) reveals a dominant theme of ESG-driven finance evolution. Her work demonstrates how ESG maturity affects climate crisis response across enterprises, financial institutions, and local governments. She bridges theoretical frameworks like resource-based view and stakeholder theory with practical applications in default prediction and sustainable investment strategies, while maintaining consistent exploration of financialization's economic impact. No scientific awards were documented in the provided materials. University service constitutes her primary documented contribution beyond research, with no available information regarding student advising, research grants, or leadership of dedicated research laboratories or teams.

• Finance
• Entrepreneurship
• Innovation Policy
• +۷ مورد دیگر

Lauren H. Cohen is the L.E. Simmons Professor of Business Administration at Harvard Business School, with joint appointments in the Finance and Entrepreneurial Management Units. He is also a Research Associate at the National Bureau of Economic Research and serves as Editor of the Review of Financial Studies. Professor Cohen teaches across multiple Harvard programs including the MBA, Executive Education, and Doctoral programs, with a focus on Family Enterprise, Investment Management, and Innovation. Professor Cohen received his PhD in finance and an MBA from the University of Chicago in 2005. He earned dual undergraduate degrees from the University of Pennsylvania - a BSE from the Wharton School and a BA in economics from the College of Arts & Sciences in 2001. Professor Cohen's research spans several interconnected domains in finance and innovation. His work on patent trolls has significantly shaped understanding of non-practicing entities in intellectual property. He has also made substantial contributions to understanding bond fund misclassification, the impact of internal deadlines on drug approvals, and how subtle changes in financial reporting ("Lazy Prices") convey important signals about firm operations. His research frequently examines the intersection of finance, law, and innovation policy, with particular attention to how information asymmetries and institutional structures affect market outcomes. Analysis of Professor Cohen's recent publications reveals a consistent focus on information asymmetry and market inefficiencies across multiple domains. His work spans financial markets (bond fund misclassification, lazy prices), innovation policy (patent trolls, drug approvals), and corporate governance (anti-bribery enforcement). A common thread is examining how seemingly minor institutional details or behavioral patterns can have significant economic consequences. His research increasingly bridges traditional finance with technology, law, and policy considerations, reflecting the growing complexity of modern financial systems. National Science Foundation Early Career Development (CAREER) Award for research on Relationships in Finance Named "Cutting Edge Academic" by Pensions & Investments (2008) Named "Top 40 Under 40 Business School Professor" by Poets & Quants (2017) Multiple Smith Breeden Distinguished Paper Prizes from the Journal of Finance Richard A. Crowell Memorial Award from PanAgora Asset Management Numerous other research awards across finance, economics, and law domains Professor Cohen serves as an advisor to government organizations including the United States Securities and Exchange Commission and United States Patent & Trademark Office, and has testified before the United States Congress. He has consulted with top hedge funds in the industry and served as an expert witness in high-profile litigation cases. His research has been supported by multiple National Science Foundation grants, including his CAREER Award. Professor Cohen has also served on the Advisory Boards of Oppenheimer Funds, Cake Financial, and Quadriserv, Inc. Professor Cohen is actively involved in executive education, having designed and co-chaired the HBS Executive Education course 'Building a Legacy: Family Office Wealth Management' and the HarvardX Fintech course. He has also designed a first-of-its-kind MBA course on Family Offices entitled 'How to Not Bankrupt Your Family.' His applied work bridges academic research with practical implementation in family offices, fintech, and innovation policy.

David Ettingerمشاهده پروفایل

استاد

• Microeconomics
• Game Theory
• Bounded Rationality
• +۶ مورد دیگر

David Ettinger is a Professor of Economics at Paris Dauphine University, where he serves as Director of the Dauphine Economics Laboratory (LEDa) since 2016. His academic career spans multiple areas of microeconomic theory with a focus on strategic behavior in various economic contexts. As a specialist in microeconomics and game theory, Professor Ettinger's research explores several interconnected domains. His theoretical work primarily focuses on bounded rationality, auction theory, industrial organization, and incentive structures. His applied research examines practical economic issues such as the privatization of highway companies, microcredit systems, and corporate takeover mechanisms. His scholarship demonstrates a consistent thread of analyzing strategic behavior under information asymmetry across diverse economic settings. Professor Ettinger's publication record reveals significant contributions to understanding strategic behavior in auctions and competitive markets. His work often examines how information asymmetry affects decision-making, with particular attention to deception, reputation formation, and strategic signaling. He has made notable contributions to auction theory, especially regarding jump bids and the winner's curse phenomenon, while also exploring applications in industrial organization and corporate finance contexts. As Director of LEDa since 2016, Professor Ettinger leads one of France's prominent economics research laboratories, fostering research in microeconomic theory and its applications. His leadership position indicates his standing within the academic economics community and his commitment to advancing economic research at Paris Dauphine University.

• Machine Learning
• Big Data Analytics
• Pattern Recognition
• +۷ مورد دیگر

Professor Junbin Gao is Professor of Big Data Analytics at the University of Sydney Business School. He previously served as Professor in Computing (2010-2016) and Associate Professor (2005-2010) at Charles Sturt University, and held academic positions at University of New England and University of Southampton. He is also a Member of the Sydney Southeast Asia Centre, demonstrating his regional academic engagement. Professor Gao earned his BSc and MSc from Huazhong University of Science and Technology (HUST) and PhD from Dalian University of Technology (DUT). His academic journey includes Professor at HUST (1997), Postdoctoral Research Fellow at Wuhan University (1991-1993), and multiple guest professorships at leading Chinese institutions including Wuhan University, HUST, and Beijing University of Technology. Professor Gao's research spans from approximation theory and multivariate spline functions to wavelet applications, and now focuses on machine learning and big data analytics. His recent work explores matrix neural networks, tensorial recurrent neural networks, and innovative approaches to data subspace clustering on manifolds. He integrates Riemannian geometry with machine learning to address challenges with manifold-valued data, finding applications in international relations, financial panel data, and computer vision. His publication record shows a clear progression toward increasingly sophisticated applications of machine learning in business analytics. Professor Gao has secured significant research funding including two Discovery Project grants from the Australian Research Council (ARC). His 260 academic papers and two books demonstrate substantial scholarly impact across multiple domains of data science and machine learning. Professor Gao supervises numerous research students working on cutting-edge topics including multivariate volatility forecasting, interpretable uncertainty systems, graph machine learning for consumer behavior, and deep learning for stock market prediction. His research group bridges theoretical advances with practical applications in business analytics, with particular interest in international relations research and financial panel data analysis.

Michael Simmondsمشاهده پروفایل

استاد

• Mechanobiology
• Blood Rheology
• Cardiothoracic Surgery Support
• +۱۰ مورد دیگر

Professor Michael Simmonds is a distinguished academic at Griffith University, currently serving as Professor in the School of Health Sciences - Exercise Science since January 2025. Previously, he held positions as Associate Professor (2021-2024), Senior Lecturer (2016-2021), and Lecturer (2013-2015) at Griffith University. He also maintains international connections as a Visiting Professor at the University of Saarland in Germany (2023) and previously at Deutsche Sporthochschule Köln (2015). His academic journey includes a Doctor of Philosophy in physiology, biophysics, and blood rheology from Griffith University (2007-2011) and a Bachelor of Science (Hons Class I) from the same institution (2003-2006). Before his academic career, he worked as a Senior Polysomnographer and Polysomnographer in sleep disorder clinics. Professor Simmonds' research focuses on mechanobiology and blood rheology, specifically exploring how artificial life support devices affect blood properties through multi-scale mechanobiological projects. His work examines how artificial materials and high mechanical forces change blood cell structure and function, with particular emphasis on sub-lethal damage to erythrocytes. He heads the GU Mechanobiology Research Laboratory, which he conceived in 2013 and later merged with Prof. Geoff Tansley's team. His laboratory integrates biological and engineering approaches to improve cardiothoracic surgery outcomes by understanding blood-device interactions. His publication portfolio reveals a strong focus on blood rheology, mechanobiology, and artificial organ development. Recent works (2023-2025) demonstrate continued leadership in understanding blood-device interactions, erythrocyte mechanotransduction, and hemocompatibility testing of cardiac assist devices. His research spans fundamental physiology to clinical applications, with particular emphasis on Piezo1 channels in red blood cells, blood quality in mechanical circulatory support, and implications for diabetes and cardiovascular health. Young Tall Poppy Award (2018) Flying Scientist (2019) Rising Star Award (International Society for Biorheology, 2021) Emerging Leader (Australian Financial Review, 2022) ISCH Research Award (2012) Professor Simmonds has supervised numerous PhD and Masters students, with current supervisees focusing on macrophage clearance of damaged red cells, nanobubbles in cavitation processes, synthetic erythrocyte analogues, blood filtration processes, and blood trauma in medical devices. His laboratory collaborates with mechanical engineers, medical doctors, and scientific bodies to drive research that bridges engineering and biology. He serves on the Executive Board of the International Society for Clinical Hemorheology and has co-directed the Perfusion and Anaesthetic Research Collaborative (PARC), recognized by Qld Health. His research is supported by competitive grants from the Australian Research Council (ARC) and the Medical Research Future Fund (MRFF). Professor Simmonds is actively involved in the Queensland Quantum and Advanced Technologies Research Institute (QUATRI) and has served on various university committees including the Research Committee and Equipment Advisory Committee. His vision centers on promoting health and life satisfaction for those requiring cardiothoracic surgery through improved artificial life support technologies that better understand and minimize blood trauma.

• Industrial Organization
• Corporate Finance
• Microeconomic Theory

Jacopo Bizzotto serves as an Associate Professor in the Department of Economics and Methodology at the Faculty of Social Sciences, Oslo Metropolitan University. His office is located at Pilestredet 35, 0166 Oslo (Room PE738), with contact details including email jacopo.bizzotto@oslomet.no and office phone +47 672 38 409. His research program bridges theoretical microeconomics with practical market applications, focusing on information dynamics across industrial and financial contexts. His core research interests encompass Industrial Organization, Corporate Finance, and Microeconomic Theory, with specialized expertise in strategic information sharing, market signaling, and certification mechanisms. He investigates how asymmetric information shapes market outcomes, emphasizing dynamic settings where agents strategically disclose or withhold information to influence decisions. This work frequently employs game-theoretic frameworks to model persuasion, reputation, and grading systems in economic environments. Analysis of his 2019-2024 publications reveals a cohesive trajectory in information economics, characterized by rigorous theoretical modeling applied to real-world market phenomena. Key thematic threads include dynamic persuasion with external information sources, reputation-fee interactions in credit rating industries, and electoral competition under strategic disclosure. His contributions consistently address how information architecture affects market efficiency, regulatory design, and strategic behavior, with publications spanning top journals like the American Economic Journal: Microeconomics and Journal of Economic Theory .

Narayana Prasad Santhanamمشاهده پروفایل

استاد

• Information Theory
• Statistical Learning
• Signal Processing
• +۱۱ مورد دیگر

Narayana Prasad Santhanam is a Professor in the Department of Electrical Engineering at the University of Hawaii, College of Engineering. His research spans theoretical and practical aspects of information theory, statistical learning, and signal processing, with particular focus on high-dimensional and complex problems that cannot be addressed by traditional statistical methods. Santhanam maintains an active research program funded by the National Science Foundation and teaches courses including Probability and Statistics, Linear Algebra and Machine Learning, and Information Theory. Santhanam's research interests center on the intersection of statistical learning and information theory, particularly in high-dimensional settings. His work addresses fundamental questions about when learning is possible, how to characterize non-uniform learning, and how to interpret data from complex sources like slow mixing Markov processes. He has made significant contributions to understanding the limitations of statistical methods in large alphabet scenarios, where traditional approaches fail. His research has important applications in diverse fields including genetic data analysis, risk management, smart grids, and text processing. His recent publications reveal a consistent focus on theoretical foundations with practical applications. Santhanam's work often explores the connections between seemingly disparate fields, bringing combinatorial and probabilistic approaches to bear on complex problems. A notable trend is his development of frameworks for pointwise convergence rather than uniform convergence in statistical estimation, which has significant implications for handling large alphabet problems where traditional methods break down. 2006 IEEE Information Theory Society Best Paper Award 2003 Capocelli Prize Santhanam has successfully secured significant research funding as Principal Investigator on an NSF award of approximately $1.1 million to examine the interplay of statistics and information theory, with applications to document classification and genetic analysis. He has also served as co-PI on multiple NSF awards totaling roughly $800,000 for research on channels with memory and smart grid organization. His research group includes students M. Asadi, A. Esraghi, A. Lee, M. Hosseini, R. Paravi, and G. Tobin, who contribute to projects spanning statistical learning, information theory, and their applications to biological and engineering problems. Santhanam has co-organized three major workshops on large alphabet information theory and statistics, bringing together over 80 researchers from diverse disciplines including biology, computer science, economics, information theory, mathematics, networking, and statistics.